With Time Series and Industry-Based Use Cases in R
Buch, Englisch, 700 Seiten, Format (B × H): 178 mm x 254 mm, Gewicht: 1337 g
ISBN: 978-1-4842-4214-8
Verlag: Apress
Examine the latest technological advancements in building a scalable machine-learning model with big data using R. This second edition shows you how to work with a machine-learning algorithm and use it to build a ML model from raw data. You will see how to use R programming with TensorFlow, thus avoiding the effort of learning Python if you are only comfortable with R.
As in the first edition, the authors have kept the fine balance of theory and application of machine learning through various real-world use-cases which gives you a comprehensive collection of topics in machine learning. New chapters in this edition cover time series models and deep learning.
What You'll Learn- Understand machine learning algorithms using R
- Master the process of building machine-learning models
- Cover the theoretical foundations of machine-learning algorithms
- See industry focused real-world use cases
- Tackle time series modeling in R
- Apply deep learning using Keras and TensorFlow in R
Who This Book is For
Data scientists, data science professionals, and researchers in academia who want to understand the nuances of machine-learning approaches/algorithms in practice using R.
Zielgruppe
Professional/practitioner
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Programmier- und Skriptsprachen
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
- Mathematik | Informatik EDV | Informatik Business Application Mathematische & Statistische Software
Weitere Infos & Material
Chapter 1: Introduction to Machine Learning.- Chapter 2: Data Exploration and Preparation.- Chapter 3: Sampling and Resampling Techniques.- Chapter 4: Visualization of Data.- Chapter 5: Feature Engineering.- Chapter 6: Machine Learning Models: Theory and Practice.- Chapter 7: Machine Learning Model Evaluation.- Chapter 8: Model Performance Improvement.- Chapter 9: Time Series Modelling.- Chapter 10: Scalable Machine Learning and related technology.- Chapter 11: Introduction to Deep Learning Models using Keras and TensorFlow.